Method for rapidly evaluating fish somatic cell or cell membrane state based on bio-impedance electricity

Abstract

The invention relates to the field of biological cell evaluation, and discloses a method for quickly evaluating the state of fish somatic cells or cell membranes based on biological impedance electrics, which comprises the following steps: S1, selecting fish somatic cell samples, and sampling the selected fish somatic cell samples; s2, performing biological impedance electrical information acquisition on the fish somatic cell sample selected in the step S1 by using biological impedance measuring equipment; and S3, substituting the cell state, cell membrane state and ATP content information acquired in the step S1 and the biological impedance electrical information acquired in the step S2 into a principal component analysis algorithm for analysis and association. According to the biological impedance electrical technology, the cell state evaluation process which needs at least two days to be completed in a traditional method is shortened to be within five minutes, the four-electrode method is matched with the broadband alternating-current electric signals to collect biological impedance parameters, complex pretreatment such as tissue slicing and dyeing is not needed, and cell state information can be obtained directly through body surface measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of biological cell evaluation, and in particular to a method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis. Background Art

[0002] In cell biology and aquaculture research, the "Golden Triangle" assessment system, comprised of HE-stained sections (histological observation), TEM microscopy (cell membrane structure analysis), and ATP content measurement (cell function quantification), has long been considered a benchmark method for analyzing cell status. HE images visually demonstrate cell morphology and tissue arrangement, TEM images reveal nanoscale structural changes in the cell membrane, and ATP content directly reflects cellular metabolic activity. Together, these three provide highly accurate microscopic evidence of cytopathology, membrane damage, and functional decline.

[0003] However, this type of method relies on high-end equipment (such as transmission electron microscopes and fluorescence detectors) and complex operating procedures. From sample fixation and slice preparation to staining and observation, it takes at least two days to complete the entire detection process. In addition, the cost of reagents consumed for a single test is high, and the professional requirements for operators are strict, making it difficult to implement quickly on site. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method for quickly evaluating the status of fish cells or cell membranes based on bioimpedance electrical analysis, which solves the problem that the ATP content, which is closely related to cell status (HE images), cell membrane status (TEM images), and cell function, needs to be measured or observed using high-end equipment, but is very complicated and time-consuming, labor-intensive, and material-consuming.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis, comprising the following steps:

[0006] S1. First, fish somatic cell samples are selected and sampled to obtain cell status, cell membrane status, and ATP content;

[0007] S2, using a bioimpedance measurement device to collect bioimpedance electrical information from the fish cell sample selected in step S1;

[0008] S3, using a principal component analysis algorithm, and substituting the cell state, cell membrane state, and ATP content information collected in step S1 and the bioimpedance electrical information collected in step S2 into the algorithm for analysis and correlation;

[0009] S4. Based on step S3, a correlation conclusion is drawn between the cell state, cell membrane state, ATP content information and bioimpedance electrical information, and the correlation conclusion is stored and collected;

[0010] S5. Collect bioimpedance electrical information of the fish body cells or cell membranes to be evaluated, and use the correlation conclusion between the cell state, cell membrane state, and ATP content information stored in step S4 and the bioimpedance electrical information to obtain the cell state, cell membrane state, and ATP content information of the current fish body cells or cell membranes.

[0011] Preferably, in step S1, sampling the selected fish somatic cell sample includes:

[0012] The cell status of fish cell samples can be obtained by processing fish tissues, making HE-stained sections, and observing them under a microscope;

[0013] The cell membrane microstructure of the fish cell samples was observed at high resolution using a transmission electron microscope, thereby obtaining the cell membrane status of the fish cell samples;

[0014] An ATP fluorescence detector is used to accurately measure the ATP content in fish cells, thereby obtaining ATP content information of fish cell samples.

[0015] Preferably, in step S2, the bioimpedance electrical information acquisition includes:

[0016] Four electrodes were placed symmetrically on the surface of the fish, and the four-electrode method was used for measurement. An AC signal with a frequency range of 10Hz-1MHz and an amplitude of 10mV was applied, and the impedance modulus and phase angle data of the fish at different frequencies were recorded to obtain the bioimpedance electrical information parameters of the fish cell samples.

[0017] Preferably, in step S3, the principal component analysis algorithm is specifically:

[0018] 1. Covariance Matrix Calculation: The covariance matrix calculation reveals the interrelationships and coordinated changes among cell status, cell membrane status, ATP content, and bioimpedance electrical information data. This provides a comprehensive quantitative description of the relationships among all data, laying the foundation for subsequent analysis.

[0019] For a given data set X=[x1,x2,…,x n ] T , where x n is an m-dimensional vector, where m represents the total number of all variables including cell state, cell membrane state, ATP content, and bioimpedance electrical information. The covariance matrix Σ is calculated as follows:

[0020]

[0021] in is the mean vector of the data set;

[0022] in:

[0023] n represents the sample size, that is, the number of fish somatic cell samples selected;

[0024] x i The m-dimensional feature vector representing the i-th sample contains all variables of the sample, including cell state, cell membrane state, ATP content, and bioimpedance electrical information;

[0025] The mean vector representing all samples is used to center each sample;

[0026] Σ represents the covariance matrix, which describes the covariance relationship between the variables in the data set. The elements on the diagonal are the variances of the variables, and the elements on the off-diagonal are the covariances between the variables.

[0027] 2. Feature decomposition: Based on the results of covariance matrix calculation, feature decomposition is performed to find the direction and characteristics that best represent the changes in these data from the covariance matrix, further improving the accuracy of subsequent judgments;

[0028] Perform eigendecomposition on the covariance matrix Σ and obtain

[0029] ΣV=VΛ

[0030] where V=[v1,v2,…,v m ] is composed of the eigenvector v i The matrix composed of

[0031] Λ=diag(λ1,λ2,…,λ m ) is given by the eigenvalue λ i A diagonal matrix composed of λ1≥λ2≥…≥λ m ;

[0032] in:

[0033] V represents the eigenvector matrix, each column vector v i is an eigenvector of the covariance matrix Σ;

[0034] Λ represents the eigenvalue diagonal matrix, and the elements λ on the diagonal i is the corresponding eigenvector v i The eigenvalue of , the size of the eigenvalue reflects the importance of the corresponding principal component;

[0035] 3. Principal component selection: Based on the results of eigendecomposition, the most important eigenvectors are selected and used to replace the original large amount of data to achieve data dimensionality reduction, making the data more concise while retaining most of the useful information.

[0036] Arrange the eigenvalues ​​in descending order, select the first k eigenvectors (so that the cumulative contribution rate reaches more than 90%), and form a new transformation matrix V k =[v1,v2,…,v k ]. Project the original data X into the new space to obtain the reduced-dimensional data Y:

[0037] Y=XV k

[0038] Where: k represents the number of principal components selected. Here, the first k principal components whose cumulative contribution rate reaches more than 90% are selected;

[0039] V k represents the transformation matrix composed of the first k eigenvectors;

[0040] Y represents the data matrix after dimensionality reduction, and each row represents the representation of a sample in the new principal component space.

[0041] Preferably, in step S4, the analysis and correlation conclusions include: correlation conclusions between cell state and bioimpedance electrical information, correlation conclusions between cell membrane state and bioimpedance electrical information, and correlation conclusions between ATP content and bioimpedance electrical information.

[0042] Preferably, the conclusion of the association between the cell state and the bioimpedance electrical information is as follows: principal component load analysis: the first principal component PC1, the contribution rate is about 45%: the first principal component PC1 is strongly positively correlated with the cell swelling degree and the T parameter, and the load coefficient is >0.8; the first principal component PC1 is strongly negatively correlated with the cell nucleus-cytoplasm ratio, and the load coefficient is <-0.7; the second principal component PC2, the contribution rate is about 25%: the second principal component PC2 is positively correlated with the cell arrangement disorder and the p parameter, and the load coefficient is >0.6.

[0043] Preferably, the conclusion of the association between the cell membrane state and the bioimpedance electrical information is as follows: principal component load analysis: the third principal component PC3, the contribution rate is about 18%; the third principal component PC3 is strongly negatively correlated with the cell membrane damage area and the Cm parameter, with a load coefficient <-0.9; the third principal component PC3 is moderately positively correlated with the ATP content, with a load coefficient of 0.5; the fourth principal component PC4, the contribution rate is about 12%; the fourth principal component PC4 is positively correlated with the membrane fluidity index and the p parameter, with a load coefficient >0.7.

[0044] Preferably, the conclusion of the association between the ATP content and the bioimpedance electrical information is as follows: principal component load analysis: first principal component PC1: the first principal component PC1 is moderately negatively correlated with the ATP content, with a load coefficient of -0.6, and the first principal component PC1 is positively correlated with the T parameter, with a load coefficient of 0.8; comprehensive load mode: the association between ATP content and bioimpedance parameters presents nonlinear characteristics, and a prediction model needs to be constructed in combination with multiple principal component scores.

[0045] The present invention provides a method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis.

[0046] It has the following beneficial effects:

[0047] 1. This invention uses bioimpedance electrical technology to shorten the cell status assessment process from at least two days in traditional methods to within five minutes. The four-electrode method combines with broadband AC signals to collect bioimpedance parameters, eliminating the need for complex pre-processing such as tissue sectioning and staining. Cell status information can be obtained directly through surface measurements.

[0048] 2. This invention uses a principal component analysis algorithm to reduce the dimensionality of cell status, cell membrane status, ATP content, and bioimpedance parameters, retaining over 90% of key information while establishing a quantitative mapping relationship between electrical signals and cell status. For example, the first principal component shows a strong positive correlation with cell swelling and the T parameter, allowing the degree of cytopathology to be directly inferred from changes in the T parameter. The third principal component shows a strong negative correlation with the area of ​​cell membrane damage and the Cm parameter, enabling electrical signal characterization of membrane damage and further improving the accuracy of predicting cytopathology. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of the method of the present invention;

[0050] Figure 2 A schematic diagram showing the connection between the muscle fibers, sarcolemma, and biochemical changes of fish somatic cells in the present invention;

[0051] Figure 3 Schematic diagram of the method for extracting the credible interval of bioimpedance (polarization elimination) of fish cells in the present invention;

[0052] Figure 4 Schematic diagram of the equivalent circuit model of fish cells in the present invention (A), cell membrane electrical information (B, C, D, E, F), and the relationship between cell membrane electrical information and cell membrane physicochemical properties (G). DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention specification.

[0054] Please see the attached Figure 1 -Attached Figure 4 The embodiment of the present invention provides a method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis, comprising the following steps:

[0055] S1. First, fish somatic cell samples are selected and sampled to obtain cell status, cell membrane status, and ATP content;

[0056] The sampling of the selected fish somatic cell samples includes but is not limited to:

[0057] 1. Process the fish tissue and prepare HE-stained sections, and observe them under a microscope to obtain the cell status of the fish cell samples;

[0058] 2. Use transmission electron microscopy to observe the cell membrane microstructure of fish cell samples at high resolution, thereby obtaining the cell membrane status of fish cell samples;

[0059] 3. Use an ATP fluorescence detector to accurately measure the ATP content in fish cells, thereby obtaining ATP content information of fish cell samples;

[0060] S2, using a bioimpedance measurement device to collect bioimpedance electrical information from the fish cell sample selected in step S1;

[0061] The bioimpedance electrical information collection includes:

[0062] Four electrodes were placed symmetrically on the fish's surface, and the four-electrode method was used for measurement. An AC signal with a frequency range of 10 Hz to 1 MHz and an amplitude of 10 mV was applied. The impedance modulus and phase angle data of the fish at different frequencies were recorded to obtain the bioimpedance electrical information parameters of the fish cell samples.

[0063] S3, using a principal component analysis algorithm, and substituting the cell state, cell membrane state, and ATP content information collected in step S1 and the bioimpedance electrical information collected in step S2 into the algorithm for analysis and correlation;

[0064] The principal component analysis algorithm is specifically:

[0065] 1. Covariance Matrix Calculation: The covariance matrix calculation reveals the interrelationships and coordinated changes among cell status, cell membrane status, ATP content, and bioimpedance electrical information data. This provides a comprehensive quantitative description of the relationships among all data, laying the foundation for subsequent analysis.

[0066] For a given data set X=[x1,x2,…,x n ] T , where x nis an m-dimensional vector, where m represents the total number of all variables including cell state, cell membrane state, ATP content, and bioimpedance electrical information. The covariance matrix Σ is calculated as follows:

[0067]

[0068] in is the mean vector of the data set;

[0069] in:

[0070] n represents the sample size, that is, the number of fish somatic cell samples selected;

[0071] x i The m-dimensional feature vector representing the i-th sample contains all variables of the sample, including cell state, cell membrane state, ATP content, and bioimpedance electrical information;

[0072] The mean vector representing all samples is used to center each sample;

[0073] Σ represents the covariance matrix, which describes the covariance relationship between the variables in the data set. The elements on the diagonal are the variances of the variables, and the elements on the off-diagonal are the covariances between the variables.

[0074] 2. Feature decomposition: Based on the results of covariance matrix calculation, feature decomposition is performed to find the direction and characteristics that best represent the changes in these data from the covariance matrix, further improving the accuracy of subsequent judgments;

[0075] Perform eigendecomposition on the covariance matrix Σ and obtain

[0076] ΣV=VΛ

[0077] where V=[v1,v2,…,v m ] is composed of the eigenvector v i The matrix composed of

[0078] Λ=diag(λ1,λ2,…,λ m ) is given by the eigenvalue λ i A diagonal matrix composed of λ1≥λ2≥…≥λ m ;

[0079] in:

[0080] V represents the eigenvector matrix, each column vector v i is an eigenvector of the covariance matrix Σ;

[0081] Λ represents the eigenvalue diagonal matrix, and the elements λ on the diagonal i is the corresponding eigenvector v iThe eigenvalue of , the size of the eigenvalue reflects the importance of the corresponding principal component;

[0082] 3. Principal component selection: Based on the results of eigendecomposition, the most important eigenvectors are selected and used to replace the original large amount of data to achieve data dimensionality reduction, making the data more concise while retaining most of the useful information.

[0083] Arrange the eigenvalues ​​in descending order, select the first k eigenvectors (so that the cumulative contribution rate reaches more than 90%), and form a new transformation matrix V k =[v1,v2,…,v k ]. Project the original data X into the new space to obtain the reduced-dimensional data Y:

[0084] Y=XV k

[0085] Where: k represents the number of principal components selected. Here, the first k principal components whose cumulative contribution rate reaches more than 90% are selected;

[0086] V k represents the transformation matrix composed of the first k eigenvectors;

[0087] Y represents the data matrix after dimensionality reduction, and each row represents the representation of a sample in the new principal component space;

[0088] S4. Based on step S3, a correlation conclusion is drawn between the cell state, cell membrane state, ATP content information and bioimpedance electrical information, and the correlation conclusion is stored and collected;

[0089] The analysis and correlation conclusions include: correlation conclusions between cell status and bioimpedance electrical information, correlation conclusions between cell membrane status and bioimpedance electrical information, and correlation conclusions between ATP content and bioimpedance electrical information;

[0090] Conclusions on the association between the cell state and bioimpedance electrical information:

[0091] Principal component loading analysis:

[0092] The first principal component PC1 contributes about 45%:

[0093] The first principal component PC1 is strongly positively correlated with cell swelling and T parameter, with a loading coefficient > 0.8.

[0094] The first principal component PC1 was strongly negatively correlated with the nuclear-cytoplasmic ratio, with a loading coefficient <-0.7.

[0095] The second principal component PC2 contributes about 25%:

[0096] The second principal component PC2 was positively correlated with the degree of cell disorder and the p parameter, with a loading coefficient > 0.6;

[0097] State inference rules:

[0098] When the score of the sample on PC1 is significantly higher than the threshold (such as >1.5σ), it indicates that the cell is in a swollen pathological state. At this time, the T parameter usually increases by more than 20% and the nuclear-to-cytoplasmic ratio decreases by more than 15%.

[0099] When the sample scores on PC2 are significantly positive, the degree of cell disorder increases, and the p parameter decreases by more than 10° in the low frequency band (<100kHz), indicating early tissue fibrosis or inflammation.

[0100] Conclusions on the correlation between the cell membrane state and bioimpedance electrical information:

[0101] Principal component loading analysis:

[0102] The third principal component PC3 contributes about 18%:

[0103] The third principal component PC3 is strongly negatively correlated with the cell membrane damage area and Cm parameter, with a loading coefficient of <-0.9.

[0104] The third principal component PC3 showed a moderate positive correlation with ATP content, with a loading coefficient of 0.5.

[0105] The fourth principal component PC4 contributes about 12%:

[0106] The fourth principal component PC4 was positively correlated with membrane fluidity index and p parameter, with a loading coefficient > 0.7;

[0107] State inference rules:

[0108] When the PC3 score is lower than the threshold (e.g. <-1σ), the cell membrane damage area is >20% (TEM measurement), the Cm parameter decreases by more than 30%, and the ATP content decreases to less than 60% of the normal level.

[0109] Samples with high PC4 scores (1σ) show enhanced cell membrane fluidity, and the p parameter increases by 5°-10° in the high frequency band (1MHz), which may correspond to an active cell metabolism state.

[0110] Conclusions on the correlation between ATP content and bioimpedance electrical information:

[0111] Principal component loading analysis:

[0112] The first principal component PC1:

[0113] The first principal component PC1 was moderately negatively correlated with ATP content, with a loading coefficient of -0.6.

[0114] The first principal component PC1 is positively correlated with the T parameter, with a loading coefficient of 0.8.

[0115] Comprehensive load model: The correlation between ATP content and bioimpedance parameters showed nonlinear characteristics, and a prediction model needed to be constructed by combining multiple principal component scores.

[0116] Prediction Model:

[0117] Establish a prediction equation through regression analysis:

[0118] ATP prediction value = 0.75 × PC1 score - 0.52 × PC3 score + C

[0119] Where C is a constant term, which is determined by the training data. Model validation shows that the correlation coefficient R between the predicted value and the measured value is 2 >0.85, mean square error <5%.

[0120] When the combination of bioimpedance parameters meets specific conditions (such as T>threshold and Cm<threshold), the ATP content is significantly reduced (<50% of the normal level), and the corresponding cell function is severely impaired.

[0121] S5. Collect bioimpedance electrical information of the fish body cells or cell membranes to be evaluated, and use the correlation conclusion between the cell state, cell membrane state, and ATP content information stored in step S4 and the bioimpedance electrical information to obtain the cell state, cell membrane state, and ATP content information of the current fish body cells or cell membranes.

[0122] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis, characterized in that: The following steps are involved: S1. First, fish somatic cell samples are selected and sampled to obtain cell status, cell membrane status, and ATP content; S2, using a bioimpedance measurement device to collect bioimpedance electrical information from the fish cell sample selected in step S1; S3, using a principal component analysis algorithm, and substituting the cell state, cell membrane state, and ATP content information collected in step S1 and the bioimpedance electrical information collected in step S2 into the algorithm for analysis and correlation; S4. Based on step S3, a correlation conclusion is drawn between the cell state, cell membrane state, ATP content information and bioimpedance electrical information, and the correlation conclusion is stored and collected; S5. Collect bioimpedance electrical information of the fish body cells or cell membranes to be evaluated, and use the correlation conclusion between the cell state, cell membrane state, and ATP content information stored in step S4 and the bioimpedance electrical information to obtain the cell state, cell membrane state, and ATP content information of the current fish body cells or cell membranes.

2. The method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis according to claim 1, characterized in that: In step S1, sampling the selected fish somatic cell sample includes: The cell status of fish cell samples can be obtained by processing fish tissues, making HE-stained sections, and observing them under a microscope; The cell membrane microstructure of the fish cell samples was observed at high resolution using a transmission electron microscope, thereby obtaining the cell membrane status of the fish cell samples; An ATP fluorescence detector is used to accurately measure the ATP content in fish cells, thereby obtaining ATP content information of fish cell samples.

3. The method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis according to claim 1, characterized in that: In step S2, the bioimpedance electrical information acquisition includes: Four electrodes were placed symmetrically on the surface of the fish, and the four-electrode method was used for measurement. An AC signal with a frequency range of 10Hz-1MHz and an amplitude of 10mV was applied, and the impedance modulus and phase angle data of the fish at different frequencies were recorded to obtain the bioimpedance electrical information parameters of the fish cell samples.

4. The method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis according to claim 1, characterized in that: In step S3, the principal component analysis algorithm is specifically as follows:

1. Covariance Matrix Calculation: The covariance matrix calculation reveals the interrelationships and coordinated changes among cell status, cell membrane status, ATP content, and bioimpedance electrical information data. This provides a comprehensive quantitative description of the relationships among all data, laying the foundation for subsequent analysis. For a given data set X=[x1,x2,…,x n ] T , where x n is an m-dimensional vector, where m represents the total number of variables of cell state, cell membrane state, ATP content, and bioimpedance electrical information. The calculation of the covariance matrix Σ includes: in is the mean vector of the data set; in: n represents the sample size, that is, the number of fish somatic cell samples selected; x i The m-dimensional feature vector representing the i-th sample contains all variables of the sample's cell state, cell membrane state, ATP content, and bioimpedance electrical information; The mean vector representing all samples is used to center each sample; Σ represents the covariance matrix, which describes the covariance relationship between the variables in the data set. The elements on the diagonal are the variances of the variables, and the elements on the off-diagonal are the covariances between the variables.

2. Feature decomposition: Based on the results of covariance matrix calculation, feature decomposition is performed to find the direction and characteristics that best represent the changes in these data from the covariance matrix, further improving the accuracy of subsequent judgments; Perform eigendecomposition on the covariance matrix Σ and obtain ΣV=VΛ Where V=[v1,v2,…,v m ] is composed of the eigenvector v i The matrix composed of Λ=diag(λ1,λ2,…,λ m ) is given by the eigenvalue λ i A diagonal matrix composed of λ1≥λ2≥…≥λ m ; in: V represents the eigenvector matrix, each column vector v i is an eigenvector of the covariance matrix Σ; Λ represents the eigenvalue diagonal matrix, and the elements λ on the diagonal i is the corresponding eigenvector v i The eigenvalue of , the size of the eigenvalue reflects the importance of the corresponding principal component; 3. Principal component selection: Based on the results of eigendecomposition, the most important eigenvectors are selected and used to replace the original large amount of data to achieve data dimensionality reduction, making the data more concise while retaining most of the useful information. Arrange the eigenvalues ​​in descending order, select the first k eigenvectors (so that the cumulative contribution rate reaches more than 90%), and form a new transformation matrix V k =[v1,v2,…,v k ]. Project the original data X into the new space to obtain the reduced-dimensional data Y: Y=XV k Where: k represents the number of principal components selected. Here, the first k principal components whose cumulative contribution rate reaches more than 90% are selected; V k represents the transformation matrix composed of the first k eigenvectors; Y represents the data matrix after dimensionality reduction, and each row represents the representation of a sample in the new principal component space.

5. The method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis according to claim 1, wherein: In step S4, the analysis and correlation conclusions include: a correlation conclusion between cell state and bioimpedance electrical information, a correlation conclusion between cell membrane state and bioimpedance electrical information, and a correlation conclusion between ATP content and bioimpedance electrical information.

6. The method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis according to claim 5, characterized in that: Conclusions on the association between the cell state and bioimpedance electrical information: Principal component load analysis: The first principal component PC1, with a contribution rate of approximately 45%, showed a strong positive correlation with cell swelling and T parameter, with a load coefficient >0.8; the first principal component PC1 showed a strong negative correlation with the cell nucleus-cytoplasm ratio, with a load coefficient <-0.7; the second principal component PC2, with a contribution rate of approximately 25%, showed a positive correlation with cell arrangement disorder and p parameter, with a load coefficient >0.

6.

7. The method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis according to claim 5, characterized in that: Conclusions on the association between the cell membrane state and bioimpedance electrical information: Principal component load analysis: The third principal component PC3, the contribution rate is about 18%; the third principal component PC3 is strongly negatively correlated with the cell membrane damage area and the Cm parameter, with a load coefficient <-0.9; the third principal component PC3 is moderately positively correlated with the ATP content, with a load coefficient of 0.5; the fourth principal component PC4, the contribution rate is about 12%; the fourth principal component PC4 is positively correlated with the membrane fluidity index and the p parameter, with a load coefficient >0.

7.

8. The method for rapidly evaluating the state of fish cells or cell membranes based on bioimpedance electrical analysis according to claim 5, characterized in that: Conclusions on the correlation between ATP content and bioimpedance electrical information: Principal component load analysis: First principal component PC1: The first principal component PC1 is moderately negatively correlated with ATP content, with a load coefficient of -0.6; the first principal component PC1 is positively correlated with the T parameter, with a load coefficient of 0.8; Comprehensive load pattern: The correlation between ATP content and bioimpedance parameters presents nonlinear characteristics, and a prediction model needs to be constructed by combining multiple principal component scores.

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